{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "8.zero_to_production.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "sYny1vUXkY37",
        "colab_type": "code",
        "outputId": "af823806-cd5b-453c-e678-1e59475a2e27",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 827
        }
      },
      "source": [
        "!pip install tensorflow-gpu\n",
        "!pip install gdown"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting tensorflow-gpu\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/25/44/47f0722aea081697143fbcf5d2aa60d1aee4aaacb5869aee2b568974777b/tensorflow_gpu-2.0.0-cp36-cp36m-manylinux2010_x86_64.whl (380.8MB)\n",
            "\u001b[K     |████████████████████████████████| 380.8MB 45kB/s \n",
            "\u001b[?25hRequirement already satisfied: astor>=0.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu) (0.8.0)\n",
            "Requirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu) (1.1.0)\n",
            "Requirement already satisfied: absl-py>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu) (0.8.1)\n",
            "Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu) (1.12.0)\n",
            "Requirement already satisfied: numpy<2.0,>=1.16.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu) (1.17.3)\n",
            "Requirement already satisfied: keras-applications>=1.0.8 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu) (1.0.8)\n",
            "Requirement already satisfied: wrapt>=1.11.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu) (1.11.2)\n",
            "Requirement already satisfied: google-pasta>=0.1.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu) (0.1.7)\n",
            "Requirement already satisfied: protobuf>=3.6.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu) (3.10.0)\n",
            "Requirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu) (1.15.0)\n",
            "Collecting tensorboard<2.1.0,>=2.0.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/9b/a6/e8ffa4e2ddb216449d34cfcb825ebb38206bee5c4553d69e7bc8bc2c5d64/tensorboard-2.0.0-py3-none-any.whl (3.8MB)\n",
            "\u001b[K     |████████████████████████████████| 3.8MB 38.9MB/s \n",
            "\u001b[?25hRequirement already satisfied: gast==0.2.2 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu) (0.2.2)\n",
            "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu) (1.1.0)\n",
            "Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu) (3.1.0)\n",
            "Requirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu) (0.33.6)\n",
            "Collecting tensorflow-estimator<2.1.0,>=2.0.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/fc/08/8b927337b7019c374719145d1dceba21a8bb909b93b1ad6f8fb7d22c1ca1/tensorflow_estimator-2.0.1-py2.py3-none-any.whl (449kB)\n",
            "\u001b[K     |████████████████████████████████| 450kB 46.4MB/s \n",
            "\u001b[?25hRequirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from keras-applications>=1.0.8->tensorflow-gpu) (2.8.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from protobuf>=3.6.1->tensorflow-gpu) (41.4.0)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.1.0,>=2.0.0->tensorflow-gpu) (3.1.1)\n",
            "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.1.0,>=2.0.0->tensorflow-gpu) (0.16.0)\n",
            "\u001b[31mERROR: tensorflow 1.15.0 has requirement tensorboard<1.16.0,>=1.15.0, but you'll have tensorboard 2.0.0 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: tensorflow 1.15.0 has requirement tensorflow-estimator==1.15.1, but you'll have tensorflow-estimator 2.0.1 which is incompatible.\u001b[0m\n",
            "Installing collected packages: tensorboard, tensorflow-estimator, tensorflow-gpu\n",
            "  Found existing installation: tensorboard 1.15.0\n",
            "    Uninstalling tensorboard-1.15.0:\n",
            "      Successfully uninstalled tensorboard-1.15.0\n",
            "  Found existing installation: tensorflow-estimator 1.15.1\n",
            "    Uninstalling tensorflow-estimator-1.15.1:\n",
            "      Successfully uninstalled tensorflow-estimator-1.15.1\n",
            "Successfully installed tensorboard-2.0.0 tensorflow-estimator-2.0.1 tensorflow-gpu-2.0.0\n",
            "Requirement already satisfied: gdown in /usr/local/lib/python3.6/dist-packages (3.6.4)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.6/dist-packages (from gdown) (4.28.1)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from gdown) (1.12.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from gdown) (2.21.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests->gdown) (2019.9.11)\n",
            "Requirement already satisfied: idna<2.9,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->gdown) (2.8)\n",
            "Requirement already satisfied: urllib3<1.25,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests->gdown) (1.24.3)\n",
            "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->gdown) (3.0.4)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8QtGS06xlJ-G",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "from pylab import rcParams\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib import rc\n",
        "from sklearn.model_selection import train_test_split\n",
        "import joblib\n",
        "\n",
        "%matplotlib inline\n",
        "%config InlineBackend.figure_format='retina'\n",
        "\n",
        "sns.set(style='whitegrid', palette='muted', font_scale=1.5)\n",
        "\n",
        "rcParams['figure.figsize'] = 16, 10\n",
        "\n",
        "RANDOM_SEED = 42\n",
        "\n",
        "np.random.seed(RANDOM_SEED)\n",
        "tf.random.set_seed(RANDOM_SEED)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5qQZwLj0lNec",
        "colab_type": "code",
        "outputId": "865a0c0a-1ba8-4a70-d9c4-f282556b7299",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        }
      },
      "source": [
        "!gdown --id 1aRXGcJlIkuC6uj1iLqzi9DQQS-3GPwM_ --output airbnb_nyc.csv"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1aRXGcJlIkuC6uj1iLqzi9DQQS-3GPwM_\n",
            "To: /content/airbnb_nyc.csv\n",
            "7.08MB [00:00, 43.1MB/s]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_xYzcw14l3mP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = pd.read_csv('airbnb_nyc.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zdHhj33mtYHP",
        "colab_type": "text"
      },
      "source": [
        "# Exploration"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TDJCd_aXpSoI",
        "colab_type": "code",
        "outputId": "e62d8873-4ab2-4e79-b961-40e560fe3ccf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(48895, 16)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F1dGRxBU3H4y",
        "colab_type": "code",
        "outputId": "6679f1da-128c-4636-8e3c-6aec44bdb50d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 125
        }
      },
      "source": [
        "df.columns"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['id', 'name', 'host_id', 'host_name', 'neighbourhood_group',\n",
              "       'neighbourhood', 'latitude', 'longitude', 'room_type', 'price',\n",
              "       'minimum_nights', 'number_of_reviews', 'last_review',\n",
              "       'reviews_per_month', 'calculated_host_listings_count',\n",
              "       'availability_365'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YGynlVbHDn0P",
        "colab_type": "code",
        "outputId": "5fb624e9-2dc1-4802-b9f8-64d82a59281d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 622
        }
      },
      "source": [
        "sns.distplot(df.price);"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 1152x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 974,
              "height": 605
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T2QZ4aQFgkyg",
        "colab_type": "code",
        "outputId": "23fa1b4d-4f4b-464b-e91f-69aceccd90ec",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 622
        }
      },
      "source": [
        "sns.distplot(np.log1p(df.price));"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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x4LcP5q7ffmNnbFx9/sB4dWd97ud4aFDoCgAAVA/thQEAAFh0+49MXDBkfa1s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\nHfrtb3+rb3/72/rSl76kdevW5W3cgYEBf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            "text/plain": [
              "<Figure size 1152x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 942,
              "height": 605
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "waXaOf0MIfv2",
        "colab_type": "code",
        "outputId": "25a6df6f-5ef8-450b-e6bf-2f5c9b7b3a4c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 622
        }
      },
      "source": [
        "sns.countplot(x='neighbourhood_group', data=df);"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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plixZkjPOOGO1dU6ePDnf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\nRERERERERERERERERGrp/wNfIcylPoxx3AAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1152x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 990,
              "height": 605
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OgblkNNNz1lx",
        "colab_type": "code",
        "outputId": "ae09b057-b581-415b-c49d-559c89e2f2ce",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 622
        }
      },
      "source": [
        "sns.countplot(x='room_type', data=df);"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1152x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 990,
              "height": 605
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tFKByUkr1BOm",
        "colab_type": "code",
        "outputId": "e6ddff5e-eb45-482e-ffbf-0ba58cb12ca2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 622
        }
      },
      "source": [
        "sns.distplot(df.minimum_nights);"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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om44oorcOGFF8JikYNHXq8X9913H95+++2vTJl09B20xnf4Ix1vOKbUokey/aUmSx3B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\nj8FhIiIiIiIiIiIiIiIiIqKvAQaHiYiIi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            "text/plain": [
              "<Figure size 1152x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 963,
              "height": 605
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_Ub0om7d1MvY",
        "colab_type": "code",
        "outputId": "c9641a78-e493-4e8a-dcc7-6e90dbacf038",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 622
        }
      },
      "source": [
        "sns.distplot(df.number_of_reviews);"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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zrndt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\n+YABA+R0WVmZVxDzQta7d285bbFYcPjw4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            "text/plain": [
              "<Figure size 1152x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 953,
              "height": 605
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WIgWS7YBvDNA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "corr_matrix = df.corr()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zZO53CSUvnbb",
        "colab_type": "code",
        "outputId": "5918f812-fbf1-4b38-a4a0-abdf7c8bd134",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        }
      },
      "source": [
        "price_corr = corr_matrix['price']\n",
        "price_corr.iloc[price_corr.abs().argsort()]"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                                0.010619\n",
              "host_id                           0.015309\n",
              "reviews_per_month                -0.030608\n",
              "latitude                          0.033939\n",
              "minimum_nights                    0.042799\n",
              "number_of_reviews                -0.047954\n",
              "calculated_host_listings_count    0.057472\n",
              "availability_365                  0.081829\n",
              "longitude                        -0.150019\n",
              "price                             1.000000\n",
              "Name: price, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Uavacdes0heu",
        "colab_type": "code",
        "outputId": "36323a45-f112-4b13-fe12-e0afba4f9fb1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 843
        }
      },
      "source": [
        "palette = sns.diverging_palette(20, 220, n=256)\n",
        "sns.heatmap(corr_matrix, annot=True, fmt=\".2f\", cmap=palette, vmax=.3, center=0,\n",
        "            square=True, linewidths=.5);"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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yLhqlHw+34ca2rU7GlvejX0BAja+vPjnK+9Ve79VrRd/evbR/i23b7VwR3cb0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\nK6+w9vald0QzZszQnj17FB9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ensZ+w0OC8eviRTiyewdmz5qJ4ICA\nFvefZ22Nt15/Db/8uID0uxW//a6PS+tSZI1NWH/sDNF2suFh5VszScZ4AAjxdMfKt2bCkc8DoHAi\nXX9Mvxm4Rvfqhl/ffhWHvp+HjR/PxmcvTMSU2N7wce4YB01JQwN+2XcUcjnAsTDHx1PGdMj3dBZm\nLBZeH0ZeN32qZt30sESAeVvJ66bXh+ln3SSUSDF7/W5sOncdiVn5ao3xAFBUJcSKo+ex51oK8ZmH\nvQ1iQ7RnlZk5qDfh4NrU3Ixvdx9HgooxHlBEEi/afwo3HyrH/fTYHrDmGLdzXFNjI87v3Um0eXb2\nmDH3G5IxHgDcfPzx0rxvYG2rcLhobm7CuX079KZHVOwQzJj3DT5auR6vf7sY42b9Dz3jRsHBTf/O\nOf81TE1M8Ey08v9rjViCTeevk4zxgGL8bDp3A0KxonyQKYuFZ6KD9aJDhUiM/TfvYsXRCzhz7wGK\nKMZ4AGhsbsbNR/n4++ItyJqUDjTDI4O0lqEDgLiIIMKI39zcjK0JibivYowHFO+R3Vdvk5yEhoQF\ngKsl2xGDcWFiYvJU/jAwqIMxyDMwMDAwMDAYDUMiAuBmxyfaG85chahe/caOqF6KDWeUho8ob3dE\nerl2uI5thWNuhqERylp3KTmFOHP3gUb5Zrkcq45fRFNzMwAgwMWRiPwwZs7ff4TCSv1587eVF/p3\nJ45zyiqx7/odjbJ7r6Ugp6ySaFPTgqrCNjfDpN7K6NAbD/OQkJ6tUX7j2WuorlOkzjUzZeH5fjG0\n9DdkTp88jqJCpYPTex/PAY/PVyvLt7HBex/PIdopSUm4ndT+jBQSiQTxO7YR7X6xAzFsxDMa5d/9\n6BPY2ik2MpuaGrFty2aNsmvW/4lBQ4fR0mPgkKGk781MT0NRYetqLhoad65eRmWZsnbl2JdeBddK\nfU1XS2sexs6YRbRzMu4jO/2+WtnW4hcaDp/gUFiwu3bDfcRzL8CCrXS0epRq/BF5p04cR5GKk+IH\nn8wFX8sY/uCTuUT7dlIikhP1MYbF2L19K9HuP3AQ4p4ZqVH+vY9VxnBjI/7ZvEmj7NqNmzCY5hge\nNGQo4lTGcEZ6Gun99rRx7NRpFBQp0+DO++A98B8bZKnY8PmY98F7RDvxdgpuJd/Wix5iiQRbd8cT\n7UH9+2FknOZ7Nue9d2FnqyiJ09jUhE3/bNMou3ntGgwbPIiWHsMGDSR9b1pGJunvY4ycS0lFcWU1\n0X5r3DPgaajpy7fk4q3xI4j2new83MnS7OzQWqL9vRHh40kq49SRbDx+FiWPr/31kUP1Wpe+KxgS\nTl43bTxzFSINBnFRvRQbzpLXTRFdsG7alnCLVPopxtddo6yLDQ9DVNZOx5PTkFFUplZWDmD1iYuE\nsdCaw8aEnhH6UbqLSLt5FdUC5fUOm/oSOBqcBLlW1oibqiyLVPAgo0212dXhFRQKj4DgpyL7j6ER\n6e1GShd/MiUD9RrKeEhkMpy8o7ynvk728Hmc9aI97Lp6Gym5RWoKNrWksKoG11UcX6w5bCLzhjps\nLbmIUnFkv5VVgAIN+wByAIcTU4m9D66FOfoEetO6BgYGBgZDgjHIMzAwMDAwGBh9+/ZFRkYGMjIy\n9FI//mliUKgySkJQW4crGTla5S9nZKOiVllXdmCo4dbujfR2g7mpKdE+e0+zMf4JZTUi3MsvIdqD\nw/SbevC/SpCrI2kD80hSqtZNCDmAo8lKI2Kktxsp04Eqvfy9wFXZWD6SmKpVF6mskVTHs3+wj85I\nA0Pn3OnTxLGjk5NO4/XgYXFwcFRGw507fardOly7nECqEfzcCy9qledyuRgzfiLRvnThHJGmuL0M\nHDKU1M7NNu70uHdvXCGO+Xb2CO+lPcouoldf8GyVm3V3bzwdZRmeYG7BJkXl19ZUdaE2+uHsKWVZ\nDkcnJwweFqdVfkjccDg6OhHtc6e1l/Wgw9XLl0ljeOqL07TKc7mWGDdBGSV98bz+xjD1HZaTrdnJ\nytg5dfYccezs6Ig4HYbr4UMGw8nRQXn+uXNapOmTcPUa6f5Pm/qcVnlLLheTximjnc9evKS3+z90\n0EBSOzvHuFPoXribRhw78K0xMCJEizQwKCIUDjyl05Xq+cZESlYuDl1TOAuFeXtg8oDeXaxR+xlI\nXTdl5miVv0JZNw3qgnVTQ2MT8gTK/5N2VpprgceG+JHmxEeStM+pK0RiXFP5GwzUEX1v6KQnKTN0\nWdvYIThGe7mM4O69YGWjdDLJSLyhRZrBEIjwdCGOhZJ6pGtwOHlCWmEZaiX1RDtc5fzOguoU48RT\n7yQCKPRTjSK+8Ui7Q1dtvRTphcr+u+L6GBgYGNoLY5BnYGBgYGBgMAoszEyJmukAkJiVj2a5dl/t\nZrkciVnKSLV+QT4dpl97cbUhR5hllVVokCSTpZJ2sneAF8xYzPSuvfSlPCc3HuqO9rrxULkBb8pi\noa8Gj33VZ1Aqa8RtHXX1qH3zuByDzvSgC2l9PRJvXCfaffsPgJlKemF1mJmZoW//WKJ9+eKFduuh\n2ocFm42eNOq2DxikNDzVCoW4czu53XoAighiVerq6jRIGj6yBikpAjwoKgamKo5G6jA1NUVwtDLz\nQ3rSzQ7Tr6toalIa/tgc9ZGmxoK0vh63VMZwvwGx9MbwgAFE+9KF9o/hhAvniWMLNhu9aI3hwcRx\nrVCIlOSkdusBqBvDIr30a2jUS6W4dkuZ3SC2X19a9z62r/LenL90WYs0fc4nKPthW1igX6+eOs8Z\nrPIMCmtrkZSiOfNNa7ClZIcQGfE7XCqTIemh0qGkd3AATE21zytNTVnoFaw0bF5Ny9QibZjUN8jw\ny15FqnpzU1PMeW4cWCzjdn6krpuS6K6bspXrpr6BXbNuMlN55lSj5amozteLq4TIr6jWKPsEVYOf\nr7M9XGzUZ/gwdGQNDchNUzog+EVEgaVjvsUyNYV/RDTRfnhHP/8DGToGMxYL/s5Kh7aHJQJaY/hh\niYBoh7g5d5h+mqCWmXuSjl4dIW5KZ81KkRiCWt3/P1XT1rvY8GCrIYMLAwMDg6HC7NgyMDAwMDAw\nGAXejnawUNn4vV9QqkVayf0CZQS5sw3PYOsFWlH0EmvZgCLLKRe9HAtzeDoYd3pNQyDARRmNXVYj\norU5UC6sQ7lQaYTxd1Vf31S17wfF5Wh8nHZPGxlF5aR6fKp9GBs52VloUHlmo7rRS8Ef1a0bcVxa\nUgJhTfvKGjzIyCCOw8LDYU6jBmFYRCTJ+JSZrp9UnyWU9MZ29vZ66bcrKCssQKNM+e7yCdYeWUnI\nBYUSx9UVAohFtVqkjQuRsAalBflE2yc4VIu04ZOdRR7D0TTHcHSMUq60pLjdYzgzQzn+wsIjaI3h\n8EjKGM7ooDFsZ7xjWBtZ2Tmkex8THaVFWomqXHFpKWqEwnbrkp6pzCIUERZG6/5HhYeR7n96pn4M\nx0UlJaS2vV37UwR3FbmlAsgalfONCB9PLdJKIn29iOOyaiGEYokWacNj08lzKKpQRGVPHzYAvi5O\nOs4wfLwdKOumQnrrprQuXjfxuRz4OCrfoarrOCqqxsr7hZrlVKH2F+DioEHSsKkoLkRTo3K+5RFA\nr164R0AQcSysrIDkKXUgexpw4lvDTMXJgo7DCQDkqcjZWnE7vc66rRXZQF6noUwGALjaKh3a8ivo\nZZDKo8i52RqnUw0DA8N/F+3uzAwMDAwMDK3g4cOH2L59O65cuYKSkhKYmprCx8cHw4cPx6uvvgpr\na/U1ZNVx+vRBqMubAAAgAElEQVRpnDhxArdv30ZFhSIC2MHBATExMRgzZgyGDx9Oq5+CggLEx8fj\n+vXryMnJgUgkgomJCaytreHu7o6wsDD07dsXcXFxsFKpuRYS0tKIoOk7//77b/Ttqzsyiy7Xr1/H\nzJkzAQB9+vTRmbZeLBZj27ZtOHnyJHJzc9HY2AgXFxf0798fL730EoKCgrSebyxQ648V0awzXlRF\nlvN2tKVtzKdDrwAvRHq7wcvBFjwuG/UNjRBKJHhQIkBKTiHOpz6EVGVzUxNUb3JLmvUyLS0sSG0f\nJzvklFdqkGagg+qzRn1+tFFUWQMnvuI9563GMYJlYgIPe2UkZSHNvmVNTRAI64g0+tpq8Rk62Vnk\ndOye3vRq/1HlcrKzEB3TvU06NDU1IT9PmXXA04ueDmw2G04uLih+XOM9R0+p5c+fUabwNzc3R2hY\nuF767QrKKLWzHVzdNEiSocqVFRbANyRMb3p1FU1NTTiweR2aHzvUWPH46DFoaNcq1U6ysx6R2p7e\n9CIoPb3IctlZj9Cte4826dDU1IT8XOUY9qL5HmGz2XB2cUHRkzGcpZ8xfI4yhsPCjbsusSYeUVLx\ne3vSM9ZS5R5l56BHt2gN0rppampCbr6Kk4sXPT3YbDZcnJ1QWFSs0CMnp806qHLq3Hni2NzcHBFh\nxut0k1smILU9Hek5l3g4kOVyywSIUjHSGzL3cvJx4MotAICvixOmD43VcYZx4NXGdRN1bqrvdZM2\nWCYmeH/0ICJCvrpOgjN31TvOOPKsYMlWroHoXl9ZjQiNTc3Ed3g52gE6UvkbIhXF5Axbds70smdR\n5SqKC+EZSM95sqvJTk1B/oN0VJYUQVIngjmbDUtrPly8feETGoHwPv2fqjr2TnxyqvcKEb3sK5Ui\ncYt+8mga8/VBuAf5GdPkSMDnckjR8xUUvTVRXSdBU3MzTB9nBXTiWyNNRyp/BsPH2EvyMTC0BsYg\nz8DAwMCgF7Zu3YrFixdDJiNH9aampiI1NRV79uzBli1b4OOjfeO2tLQUH330EZKSWqZQE4vFyM/P\nx+HDh9GzZ0+sXLkSzs6a03Bt3boVP//8Myma5wmVlZWorKzEvXv3EB8fj9dffx2fffYZzas1HNLS\n0vDuu++isJC8KM/OzkZ2djbi4+Px5ZdfYvDgwRp6MB5cbMgOHarRyNooqyHLudjw9LqxFOJOfgbN\nuabgcdnwsLfF0PBAzBraF/9cvIljydpralKjsP2dHfCoVHfaen9n8iaoqqc5Q9twVnnW6D5nAFCm\nIuuixlvf3toS5mbKSIfW9v3EIO9sQ9+5ydAoKSZHkrq40ttAdHUjG2yLi4rabJAXlJeT/le50DQa\nA4CrqxthkKdeS1tIvXsHiTeVNTxjBw+BVSuc1wyNKgF5Q8zWgV6Uoa0jWa5KUG60BvlGmQw1lRXI\nybiPyyeOoiRfYTi24HAw/f054FoZ7/0F9DuG22qQb9cYdnMjDPLFRe0fw/fu3CGl8B84ZKhRj2Ft\nUCPB3Vzo1W51pzwjRcXF7TLIlwsEpPvvSlOPJ7o8McgXFdOLqNVGyr1UXFdJ4z90YCysrTTXyzV0\nSqvIhhNnWxsNkmRc7MhypVXVRmGQl8pkWLbnCJrlcrBMTPDJc+NI8zRjhrpuKjOQdRMVM1MWHHlW\niPRyw6TeUUTUu1jagJ8OnIZIQ3QtNdU83Tl1s1wOQa2IWC8Za8r6mgqy8wzfnl6kP9+enGWrpkJg\nNAb54hyyE51ULIZULEZVWQnSb13Dxf27MHDSVHQfTC9ww9ChRprXiOs1SJKppmQosbXidppB3taS\ng2hv5ZyssLJGY6Y5WysOqU33+uQAhJJ62FlZPu6HSVnPwMBgXDAGeQYGBgaGdrNnzx4sWLAAABAY\nGIjw8HBYWFggIyMDd+8qaskWFRXh3Xffxf79+zWmlSwtLcX06dNJxuWgoCCEh4fDxMQE9+/fR+bj\n9JKJiYmYPn06duzYodYof/LkSUInAODz+YiJiYGLiwtYLBaqq6uRlZWFR48eoVlNyugZM2YAAA4c\nOEDU8508eTIpiv4JLq3YCNQnubm5eO2111BVpUzbFRERgZCQEDQ0NCA5ORmFhYWYP38+vvjiiy7R\nUZ9wKZHgIqmU1nnUNGlcmpHndGlobERRpRA1knqwTAA7K0u429sQXr42lhy8N3oQQj1csPzIeY39\npOYXExuCADA0IhCnNESFPMGZb41Ib7Ihgm5kPYN62OZmhMc9AIjqNafZo6L6rKl7zlQjeQCgrhV9\nq2ZQoPZjTFDro1vz6G2EWlmT5cTittfoFbdRB4Ueyg1ucR29SA6NeojFWPT9d5A/rgdpamqG/3v7\n3Xb12dVIJeRNQK6lJa3zOFyyHLUfQyblagJ2rV2p8fcsFgthPXpj9LSX4eBC33BsqNRRIrR4NMeP\nNY9sHBKL2z5+qDXaWzeGlbLteY8ozhdj4fffKMewmRlmv/Neu/o0ZKi10Xk8eo4H1tbkuXNdO+69\nQg/y+bxWOECoGsvbq4dYLMY3CxcR99/M1BTvzf6/dvXZ1VDnzFZcetGmLcouaUlRbEhs+fcCCgSK\nrFKTB/RCuLeHjjOMhxZzTprrJuq90/e6aXBYAD6bpNlg2tTcjGsPcrH53HUUV2sub2HJJuulLS02\nFbGO+boxIK0nz5M4NOdbbC7ZeNlQbzzzLVMzc9g5u4BrzYNc3ow6YQ2qykqBx+9gSZ0Ip7ZvQVHW\nQ4x9dTZMjDzilm1GNtnUNzTSOk8qI8tZmHWO6ccEwOTeUSSnptNa9jJaXh+9cn0A+RotnhInKgYG\nhv8OjEGegYGBgaHdzJ8/H46Ojli2bBn69+9P+t3p06fx0UcfQSaT4cGDBzh8+DCeffZZtf18/vnn\nhDHexsYGS5YswdChQ0ky586dw7x581BbW4uCggJ8+eWX2LhxY4u+fvvtN+J41qxZ+OSTT8Bmt9xU\nqqysxMmTJ1ss2L799lsAwPnz5wkD0vvvvw9Pmuk5Oxq5XI6vvvqKMMY7Ojpi5cqV6N27N0lm+/bt\n+PHHH7Fs2bKuUlVvUDdMGmS608ADgLSRvCilpnhvCzXieuy9noKrmTlILyxD8+ONgCfwuWyM6haK\nFwd0JzbERkQFQyCsw98Xb6rtUyiR4tajfPQJVKTe7e7niWERgTiX+lCtPMvEBO+NGUQyHgPGu7Fk\nKFDr7DU00tv8AIAGlc0BqgOJ4jPy1Jv6bGpDVdaY77GEYgBhszkaJMlQ39/tMYaLJVQd6Ke3VJVt\njzFPLpdj0fffIi83h/jszbffhrevb5v7NASk9eToFjOa71tzipwxbRDrIiSmJ/oOH/VUGOMBQEId\nPxy6Y5gsR3WMaZUOLd4jbRzD7XiPyOVyLPzuG+SppD3/v7ffgY+Rj2FtiCmOMhyaf3eqXLsN4ZRn\nkK4eAPUd3r77//XCRcjJyyM+e/f/3oSfD73yCYaKhGLUZJvRm29YmJPnN9R+DJH7eYXYm6DIUONq\nZ4PXRg3tWoX0DIc6n23jukndfLYjufEwD8eS7ms1xgMtr49qhNSGaikxY51Ty6SU+ZY5vftElWuo\npxeV3FVY8njoPWIMgmJ6wt0vECxTsvFVLKrFnYTzuHr8EPE3Sb2WAJ6tPQZPfr4rVNYbVENzYxO9\nMSyjyLE7yWA9MjoEvk7KzH23HuUjW0sZPaqjQKOaIBlNyJqUslTDPgMDA4Ohw7y1GBgYGBjajYmJ\nCTZv3ozg4OAWvxsxYgRmzZqFDRs2AACOHj2q1iB/+fJlXLlyhehv7dq16NWrVwu5YcOGYfXq1Xjt\ntdcgl8tx6dIlXLt2Df369SNk6urqkJGRAQBwc3PD559/rtFD2t7eHtOnT2/9RXcxFy9exM2bCsOu\nqakp1q1bh8jISJKMiYkJZsyYgfr6eixZsqQr1NQrZhTDcxPNRVtzM9lYbmrK0iBJn58OnNb6e6FE\nivhrKbiSmYPFL42HA08RkTW1XzecuZeJQg11DrdeuoXufh4wf7zZ8PG4oXDiW+PgrXukjSYPexu8\nPTIWPfxaOohQDfQMrYP6fNB9zgCgScUxg/q8Ai3vTRPl2dSG6nNszPe4kbLRa2pKb5OIKtfUCmcG\nKtRz6eqgkFUun9qjw5oVv+DC2TNEu9+AWMx49bU292coNFM2AVksen9bFnXc0dx0NAR4tnYIiooh\n2g3SelRXCIh0smlJN5GWdBN+YRF44a0PYEMzrayhoq8xTO2nPTqYtWoMK2Xbo8Oq5ctwXqV2fP/Y\ngXhl1utt7s8Y0N+9b9/4pp5v2gpjgxnp/rddj2Wr1uD0+QtEe2D/fnj9lRlt7s9QoL576c43qHJ0\nDUddRUNjI5GqHgA+mjK20w3PHY1ZG+ez1HWTuvlse6gUiZGYlU+0OebmcOJbE+WY+gf7on+wL+7k\nFmHZ4bMa60q3uD45/Tm16t9C39fXWVDHKovmdVAN2tR5m6ExafYHWn9vac1Dv9ETENy9F3YsX4S6\nGkVq9hv/HkVk/4GwN2JnSOp7lRoAoAnqGKb7bLSHvoHe6B/sS7SLq4Q4kZKu9RxTFnl/jqq3NlT/\nFiyWcWdCYFBg7BktGBhaA2OQZ2BgYGBoNy+88IJaY/wTpkyZQhjk7927p1Zm165dxPG4cePUGuOf\n0L9/f4wePRrHjx8HAOzcuZNkkBeJlKlM7ezsnsrJ3d69e4njSZMmtTDGq/Lqq69ix44dyM/P1yhj\nDNTLyGnMLMxMSREOmqDWgqT205EUVtbg54NnsOTliQAUm0cTe0Xi938vq5V/WCLA2pOX8f6YQWCZ\nmMDMlIVZQ/tgemwP5JRXQtIggyPPCp4OtsQ5e66lYGq/bkS7NSkbGVoibfGc0Z8um6tscql7zupb\npBCkb0TQ1bexwKWmymyQgsPRXfuvoYH8XHO4ba8XSP0+at/a9VCmfG2rDps3rMPu7duIdkRUFOYv\nXvJU/K8yp0SqNspksKARvdpISVNJ5xxDwT8sAv5hES0+rywrxeWTR3H99Ak0NzcjOy0VG378Fu/M\nXwxLa+OsWQu0fO4bpFJaY0F17AAt3wXt0UHaqjGskqq4jTpsWv8Hdm3bSrQjoqKx4OelT8UY1gaX\n0/LvzqWRIYF6f7hcelkVNOtBPl/ainmPlHT/26bHH5s2459du4l2dEQEli2Y/1Tcfw41W0ljIzg0\nIoip2YSo/Rgaf526iLwyhdPUyB7R6BXs38Ua6R9DXTfdyy/GvfziFp+72vAwsXckxveIgCmLhWgf\nd/w8YyI++ms/RPUt0+23mFO3wjFLdf5N7cdYsLCgzLcaZTC3oDHfkpHfl9R5m7Fi7+KGiW++ix2/\n/AgAaG5uQuLZf/HM9Fe7WLO200AZr2amLFJkuCaoToqydjrB6aKbjztGdwsl2hW1ddh2OVFnxLu6\n66OLqiNNR18fAwMDg75hDPIMDAwMDO1m9OjRWn/v7+8PDoeD+vp6VFdXQyQSwZpS7/FJtDcAjSnt\nVXn22WcJg7zquYDCCM9msyGVSpGZmYnk5GR0796d7uUYBTdu3CCOJ06cqFXWzMwM48ePx++//97R\nanUoEkrdNLa5Ga2NJWoaM4m0c42Z9/JLcC+vmKj13tNfe9mDkynpEIoleH/MYNhaKTa/2eZmCHF3\nJsnJmpqwPSEJx5PTSAZ5dZtWDPSRUAyDbHP602WOiiy1H6BlbbzWpNhj6+jbWOBSaoXX19fTMsjX\nU1KYW9KslalWB0vy99W3Il2nqqylpZUWSfVs+2sLNq37g2iHhodj2erf2nU9hgQ1fbmsQUrLuE41\n1rJpPBOGjr2zCya88joCI6KwdeVSyOXNqCwrxbHtf2Pq7He7Wr02o3YM0zBsU8eZpVXbn3nqeKGW\nSqCrR1vG3dYtm7HxD+V8KjQ8AivWrDW6MZz58CGW/6Z7XvjJu28jODAQAGDJbfnupGOQp957q3b+\nrSyp73Bp593/TVu3Ye3GTUQ7IjQUv69YZpD3P6u4FOuPn9UpN3tMHPzdXAAAXDY1DbiMlkFeSpmX\nUOuXGxLp+UWIv3QNAGBrbYW3xo/oYo06Bmq96TavmzppzllSU4v1p68iJacIXz37DExZLLjZ8fFm\nXD+sPHahhXx75uuq12isc2pzNnW+1UDPIE9xkLKgWXbGGPAKCoVnYAgKHioyJWbfv9PFGrUPqqOT\nuakpLYO8OcWw3ZoSaa0l0ssVk3pFEg5pVSIx/rp4C6J63Y5yVIO8eSucalRl6bzXGBgYGAwJxiDP\nwMDAwNButEXHA4r0Q3w+n9gEq6urIxnkS0tLUVlZSch269ZNbT+qdO/eHSYmJpDL5RAIBCgtLYWL\ni2IzycLCAnFxcTh+/DgaGxsxc+ZMjB8/HqNHj0avXr1gZdV6I4ohUVJSQtSONzExQXR0tM5z6PxN\nDZ0aSu1Se2tLCCW6jc/21uRNUqGk82vlpeQWEQZ5V1s+TFkmWtOVX32Qi6ScHRgRFYxe/l7wc3YA\n35KD5uZmlAlFSMoqwInbaSiorIGXSrQ8AJTV1HbotTztNDXLIaqXwpqj2NSyb4XRyE7lWROKWz5n\n1GeP+mxqw15H38aCrZ0dqV0hEMDW1k6DNFlOFRtbWw2SNHSwpepQTvtcVT1aq8OubVvxx+pfiXZw\naBiWr/kd1kYcLU3FiscntWurq1p8po7a6ipS25L39PxNwnr0Rs8hw3DrvKJEwe0rFzFuxixwjXQu\nYqduDNvRGMPl1DGs+xxNUM8VtGoMK2Vt7Fo3hndu/QdrV60k2iGhYVi59g9YG+HzWiOsxZXrN3TK\nvfHKy8SxHeXvVV5RATsa70FBRQWpbWdjQ1NL9djZkL9TIKjQINmSchVdWqvHPzt3YeVapUNVWEgw\n1q1cDh7FydhQEIrrcSszS6fctCEDiGMbynupolYEGxrzoMpaEanNtzRcp6o1h04SqZHfmzjSoHVt\nDzVi8rrJzkjWTdcf5uLUnQyMjgkDAAyLCMKGM1dbZACjzoXbPKfugnWhPqDOk+pqqmll3xE9Tun+\nBK6V8f3/0oZ3SDhhkK8WlKOpqZFUbsqYEFOCCKw5bIhpOJDwuGTHDHEHZc8L93DBlN5RYD02xlfX\nSbDl4k3aY4qq15O1Nx1Ur1HciixJDAwMDIaAcf5XYmBgYGAwKHg0NiLNzZXRFTJK6rsnxngA4PP5\nLaLnNX0nj8eDUCgEAFRVVREGeQD48ssvkZqairy8PDQ0NGDfvn3Yt28fTE1NERYWhj59+mDw4MHo\n27dvp9TV0ieqfy8bGxtaDgZubsZbP+0JBRXkDQRnGx5yyqs0SKvKkZ+nfEo/nUGlSv1DlokJ+FwO\nquokWs4ApLJGHE26j6NJ97XK+TiRDRPpRWVtV5QBgOJZC/VQvE+oz482nPlK2fzKls+ZqL4BlSIx\nsRHYqr5VZKljwZjw8fUltUuKixEQGKTzvNJicnpTH1+/NuvA4/Nh7+CAyseGmZLilqlTNepRopT1\nplyLNvbs3IE1K34h2kEhoVjx2+/g8XUbq40JJ3cPUrtaUA5XLx+d51VTHC6o/Rg70X1jCYN8c1MT\nCrIekOrOGxPUsVdSXISAIN1juKS4SGs/rYHfjjGsKtsaHeJ3bseq5cuIdnBoKH79fR34T9kY1oaf\nD3ksF5eUIDggQOd5RSWlpLavj+53gjb4fB4c7O1R8Xg+XFRSQvvcYhVd/Hy8aZ+3PX4Plq5aQ7RD\ng4Ow/tcV4POfLmOWl5MDqV1WVQN/V2cN0kpKq2pIbW9nR73qpU+qauuI44Xb92Ph9v20z/3nzCX8\nc+YS0V72fy8jJqB9z3NH0WLdxOch10jWTZfSsgiDvJkpC8FuTkjOKSTJFFZWo6m5maizrToH1wbX\nwhw8lXIVXXF9+sDe1Z3UrqkQwMnDS+d5wkryfIvaj7FjreqwJZdDIhKRPzMiBCrvKgCwteKiTCjS\nIK3EhuJkRO1HH4S6O+O5vtHE+Kuuk2DLhRuoaYXTeIWoDs1yOWHQt7Wil63BwswUXJXMLQKh/q+P\nofN5Gsr+MDDQhTHIMzAwMDC0m/ZOnurqlJPo1tTz5HK5hEFetQ8AcHZ2xr59+7B+/XrEx8cTEeVN\nTU24d+8e7t27h02bNsHDwwNz587F2LFj23UNnYlYrDTucmimmWtPrVZDgbqJFODiiBsP83SeF+iq\n3BSUNTahuEqod910QU2jqM/UcaHuSkeU6joJSqqZCPn2kiuoIgzy3o52MGOxdNbBMzNlkZwj8gXq\nNz3zBFWEQT7Ahd6GtTPfGnyVzcM8I908BABff7LxJjM9DbGDBus8LzM9nTg2NzeHh5fuTUddejwx\n5mVmpOuQVlBSXARhjdLo4OtHr+bsvt278OuyJUQ7KDgEK9f+AX47o0QNEWfKZnBhTjZCu/fSeV5R\njjKK09TMDA4urnrXrSuxdSCP9bpa431P+1EMsBnpaYgdPETneRmUMezZzjHspzqG09NonVNc1LYx\nvHfXTqxY8jPRDgoJwarf1xv1GO7dozvuXLmkW1CFQD9fUjstIxNDYmN1npeWkUkcm5ubw9uz/Q43\nAX6+hEE+LTNTu/BjiopLUCNUzgH9aTpV7dy7D4tXKLObhAYFYcOqlbAxcGeMmAAfnF78VavO8XVx\nIrUfFJagX5huh5sHRUqnCHNTU7g7tD0DBoN+yKXMQwNdHXHzke51k+rctKvWTVSjI9+y5XpX2tiE\nshoR3OwU4zDAld6cmjr31jRfN3Qc3cjv0dL8HARG6y7RV5qXQxybmpnBztlFs7ARIqOUQDK3MNzy\nGbqgjgM3Wz4yi3VnBHKzVf5vamxqJgUG6INQd2c8368bxRh/E9WtzOAma2pGdZ2EWBer6q0Nqlx5\nrW4nBQYGBgZDwrhCAhkYGBgYnkpUI7wlEu1Rw6qoyqqLEufxeJgzZw4SEhKwfft2zJkzB0OHDiVF\n9BcWFuLjjz/G+vXr26h956Nap5Ju7ePW/F0NFUFtHWlTKMqbXtR/lJdSLrWgBM1yzaniOwpVQ229\nrLFFCrr2MChMaVA4e++B3vr9L3M3TxlByTY3Q4i77uiwUHdnWKjUpLyTqz5iU7VvNzs+HHm6M1xE\nUp71O7lFGiQNHxdXV7h7eBLt20lJtM67nZRIHEfFdIdpK+oMqiOmRw/iuLiwEKU0IixvJyaS2t17\n6jY079+zGyuWLCbagcHBWPGUGuMBheHZXmVzNzs9ldZ52enKTCA+waFgsdp3fw2Negl5M9RY09UD\nj8ewp3IMJ1PGhSaSE28Rx9F6GMPde/YkjovojuEksq49evXWec6++N345eefiHZQsPEb49uKq4sL\nPN2V0ZS3km/TOk9Vrnt0VLvvPQD06q7MMFFYVIyS0lIt0o/1uE3Wt3cP3carXfv2Y9EvK4h2SFAg\n1q9aYfDG+LbibMuHm70ymvROtm4DLgDcyVLKRfp6EYYaQ4RnyQHfkkv7RxW2uRnpd2amhnud1HVT\npBfNdZN316+brNhkI6qmetR385Vz6hA3Z1r3Q/X6JA0yWgZOQ4Rv7wBbR+X6JD+TnnNp/gOlnEdA\nsNFlCtSFoKiAODYztwCbS7+UgaEhlNSTjOnUrHia8FWRy6uogj5HcIibkwZjfNv2mnLLlZkfPext\nYMrSHejj62RPHDc0NqKosvOdhhgYGBjaw9P1n5eBgYGBwSixt1dOqoVCIUQi3V6utbW1qFWJMKPW\nNFXFzMwMPXv2xOzZs7Fu3Tpcu3YNf/75J3r3Vm7Erlq1CsWtSHnalaj+vWpqalpkB1CHsVybLq5k\nZhPHUd5ucOJrN2o48a1IxszLGdlapDsGroU5+gUp01mmFdBPraqL4VFBcHqcorFZLsfx2/SiBBm0\nc+NhLmRNTUR7eJTu6LDhkcHEcaVIjPsa7vMVyjM4PCpYrRy5b+X3ZxSVdUjqwc5k8LBhxPHtxFuk\nNPDqKC0pJhnShsYNb7cOQ4aR+zh59IjOc04cU8qEhUfAxVV7FPfBvXuw4melMT4gKAgr165rde15\nYyO8Zx/iODvtPqp11PeuFpSTDPKRvft1mG5dRXYa2THBwdm4MwAMGRZHHCcn3tKZMr6kmDKGh49o\nvw6U98DxI4d1nqMqExahewwf2BOPXxYvItqBQcFY9cf6p34MayNuiDKjya3k26QU8OooLilFoooh\nfMTQoXrRY/gQclaGQ8dP6jzn8PETxHFkWBhcXbRHhsYfOEgyxgcHBmDDqpWwfcqdMQZGhBDHKVm5\nKK2u0SINlFbXkAz3AyNDtEh3PX988Cb2ffsJ7R9VXhjcn/S7SN/2ZfroaK62Zd2kYrinzlk7C6rT\ntaYofVX9OBbmGBiiO+vJsAjlnPrWozzSfN/YCIpROqblZ6a1SEdPRVgpIBnuQ7rrdkozJqT1Ejy8\nk0y0PQJ0r98MnfQi5f9YXyd72HC1Z0e04XLgo2KwTivQ7axGl2A3JzzfL4YwxteIJfjrYtuN8QCQ\nplJqz8LMDOEeuufH0SrvhwfFAp1Z7BgYGBgMDcYgz8DAwMDQ5bi4uBBGZrlcjpSUFJ3n3L59G/LH\nHvuOjo6k+vG6MDMzw8CBA7Fp0yYEByuMYTKZDJcvX26D9p2Pq6sr4YAgl8tx9+5dnefcuXOno9Xq\nFE7dyUTT40WXKYuF6bE9tMq/FNuTWDSKpQ24lPaow3Wk8sawvqRabgnp+tnccrXl4Y1hSsPVieQ0\nFFZq3zRloIeovgFXM3KI9tCIICIlpjrc7fgYEhFItE/fydAYjZArqEKGyubDhJ4RsOawNfYd6eWK\nbr7KtJT/pmTovgADZ+yESURETnNzM7Zs3KBVfvOG9Wh+PO4traww7Jln2q2Df2AgwiIiifbe3TtJ\nqayp3E5KRNLNm0R73KTJWvs/cmA/flm8iPg/FRAUhJW/P/3GeADoOTgOJiaK+yuXN+PswT1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eUWi31vueUW43fr16+XPReOITY2VosXL7Ybf/PmzXrggQc0atQo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\n6pcfTCMax157vXwr6QzZt3O7/jn+cuPnq0/m2czHD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6cOCANm3apKVLl+rxxx93eps//fSTHn/8ceMgDH9/f5133nmKjY1VdXW19u3bp61bt8pgMGj5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\nP0tMsy3DA/312OSR8vH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rxtuPnqBMIlGLLvWJa7fvEMe2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\ndHQ03r2TpsSOiYnB6NGjcfToUVhaKr/xXrp0KRGMDwwMhK+vL7S1tfH69et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gI50Pdy/5\nJfiqyGT4UlWuNU1MzWBhaUUEXjPT2dsxg6KHswwdjE1MYGVjQyyENTQ2ptWdV4SZmQVSIQ3IFxVq\n5lwFAEcX+vmdnZEOVxb3l9kZdDs4utZ+nhibmsLcwpIo8ZKVwd4XZ1H0YH4WAMjJykRqUiLRbta2\nPatFMF4N/GBpbUMs1IuJiuQC8hwcHBoFF5Dn4ODg4Kh3ODg4KJVxdHQkjsViMYqKimAqZyeqmZkZ\ndGuQrlQR2ZSV487OzrVeeZ+ZST5sEggEOHjwoAJp2RRo2MMkL8YDo5iYGHTs2FFpv5iYGOKYx+PB\nVYWb0io9quwYGxvLqk9aWhry8/OJtqdn7QPG9RlPb/qDpNiYV2jfqbPSfrEMG7uoaGNPL28iIP86\n5hWrPmmpqSig2NjDk1092pNHj2DdH6uItq+fHzZu2S5zt0h9gFmn901sLNp0UD6P376m29jJRbWH\npO6eXkRQ5k1sjBJpKelpaSik+E03Nc5jO3v671Z+Xj5c1FMlRe009nLDtaWza9TH3Y5eB/JNajra\n+CsP8L1NJR/Y8nR04GRd++wXdRVmLXT9erpb90OQkJVLBOTdbCyhq62Nsso0tfLQ1dGGuy15HiVl\nyd55m5iVSwTkve1tZMowsTMzgRkl1Xaihgbk3Rm/X29fx6J1+w5K+717TV4T6fJ4cHRWzU+7Ufz0\nO5bXW+l8hp9WYYGeptOiUUOEXj5boz5e7vTrp5i379CpdUul/WLekeU2eLq6cKXcZ9UWLzc3IiBP\nHV8RaekZyC8spIyhvoAtNRW+vv6nL5/SsmkTRNy+XqM+3p4etParN2/QqV0bmbI0uddviGMejwdX\nZ9UXoXh7ehAB+Vdv3igWriSVn478Aop9PdS3cNHB3o7Wzs3Pr9MBeaZve//6NVq2ba+03/s3r4lj\nXR4PDs6qLZ5x9fAkAvLv37Dz0xl8PoooflreAlc3Dy8iIF9WgxIw1Pr0enqam8rcmbGgLP7tGzRp\npXy+xr97Sxzr6vJUXjTm7O5OBOQT3rKbq1kZ6Sim+GLmAhIAyMmkZ6GzslG+UJeQtbUlAvL5uewz\nbHBwcHDUBbQ/tQIcHBwcHBzqhlmXjo1MSUmJXFk2dTPZUlxcTByrUluzkHKDU1uq6s9rCg4ODnCm\nPDQICwtj1Y8q16RJE+iomAq4WbNmxHFKSgqrTAOhoaG0dvPmzVXSob5i7+BAC7SGM743eYSHkik7\nGzVpqrKNm1Lsk5qSUm3Xnywiwui6Nmuh/AH2qePHsGbVCqLt26B+B+MBwM7eHo6UeRwZzm4eU+WC\nGjVW2caNmpLzmJ+aigwWu3qYujZupr55XFxET81uoqY0u3UFOwszOFLKOETGJ7HqFxlHygW5u0BH\nu/7dvsankw8k9Xm6MGZRr5lDSlQimbFCn6cLPyc7BdJS/J3soEdZZBmZIDsLCnVsR0sz2JgaKx07\n2I0ehIxMSJUjWbextbeHgxPpp6MilKc9Z8qpw083bNKUOOanpbLafRkVTteV6uurMDM3Z/0yMaVn\nqjE0NKL9XVu7fpWXcLC1hTNlYXNY1AtW/UKjoonjJkGBKtseAJo1DCKOU/jp4GcqLyFE1QMAmjcK\nVlmPKt7Fkzs5rVmUU6qLONjZwcWJ9FOhzyNZ9aPKNW0YrBb7Nm/ciDhOSeODz2Lnbejz57R2iyaN\n5UjWnCLKPTogzdJQl7Gxs4e9ExloffE8glW/aIpcQHAjlW0Z1JjMTJielkbbFS2PF8/pfjqY4uup\nNKTccwsEpchjUbqioqICGZRsVpZWmptZydrWDrYO5HyNjWY3X2OjSLkGQUHQVtHGfsHkXM1M5yM7\nU/lcjYmi6+rfsPpc1WUsQBWLalefXk9ffc/qODg4OD4G9e+JBgcHBwfHf57S0tIay6gSHK8Jxsbk\nA11FiwCUQdXXxsYGsbGxNX79/PPPKn2WT0HXrl2J49DQUKXBcD6fTwvId+vWTa06AMDFixeV9rl0\n6RJxHBgYyCqLw3+Vzl1JG4WHPlOaMp6flkYLhnfp3kN1Hbp1p7UvXzivtA9VJiAoCPZKbHzmxHGs\nWbmcaPv4NsDGrdtVqo2uKbTv1IU4jgwPU5oyPoPPRyQlyNKxq+rzuEMX+jy+dln5PL52mZzHfgGB\nsLO3V1mPKqifT1dXt9qO+fpA+0Cy/ERkXCIy8hRnacnIK6AF7jsGNfhgun0qSoRCPIohdzIFuam2\nU+2/RsjbBIgpiwu7N1Re4qR7MHke5RSV4GWybP/zMDaO3q+h8vOvezD5/2NTM5BVWKxAum7TjpKd\nJoqln6YG5Dt07qKyDu0ZY1y/ckm2IIUbFJkGAYGwleGnj168yvq1afdeWt+fpk6j/V3W+JpOV8qO\n6dCoaKRlKA6Ep2Vk0gL33Tq0VYse3drTx7l4/ZbSPhdvkDJBDXzhYMt+x6Ui0tIzEP7iJdFuEhSg\nlnE/Bd06kNkunkU8r5aOnklaejotIN+9k/JsGWzo3pE+zvmr15T2uUCRCfL3g4Od8kVYbAmNID+j\nrq4uHNU49oeiTYdOxPGL5+HV0tEzyUzn4yUlIN+WRRYypTowxrj17xWlfW5TZHz8A2BjJ9uPtu1E\nv1aPePpE6dhvYl6hiLJxwS+oodI+dZnmlKwHr6Iika1k4Up2RgZiKIH7Fu2UZyFTRot29Ln64Iby\nuUqV8WzgB2vb6vPJwopezur9a3YZFkpLSpCWRN4fWFmzy2LEUbfR0tKqly8ODllwAXkODg4OjnoH\nmx3L1NrfPB4PJiYfZxW8jQ15w5CSkoKKiopajWNtTd7A5OfnQyxmn8ZNkxk4cCC0K3dIlpeXY+fO\nnQrld+zYgfLK9LXGxsbo2bOnyjr4+PggKIjctXPs2DFaOnomYWFhePr0KdEePHiwyjrUZ/oPGkyz\n8Z6d2xXK79mxjbCxkbExuvfspbIO3j6+CAwmd1WdOHqYlo6eSXhoKEIr638DwMDBQxWOf/7MKfy5\nYhkx//9LwXgA6D1gAM3GB/bsUii/f/dO0sZGxuishkUXnt7e8A8k5/HZ48cU2jgyPAwRlEwMfQYO\nUlmHKlKSknD1wjmi3aR5C1aZXjSN3s0aQrvywUR5RQUO3HqgUH7/rQcor5wjRvp66Bzs/8F1/Nhs\nv3Ib+SXkAsGOQfXvM35IigQiPIqNJ9pdgnzhaGkmV97J0gydg8hSCdcjYyHvKiwhKxexqeSD74HN\ng2CiIHtBsKsDGnuQCyr+fc7uwXJdpVc/up8+/M9uhfKH9u4i/LShkRE6dlPdT3t4ecMvIJBonz95\nHIUF8v10VEQ4nlMW6PXuP1BlHf6LDOrVnX6tffioQvkdh46Q19qGhujZUT0BWx8PdwT5kYtcjp6/\nSEtHzyQs6gWePo8i2oN7q34OAtKMYiv+3oqysjLive4d2qll7E/BoL69afbdvu+AQvnt/xwg7Wtk\nhF5duqhFDx8vTwQHkL95R06dQb6CcmqhzyMREk4Gk4f266sWPQAgMTkFZy+TQeJWTZtoxHVYtz79\naLY8tv8fhfLH9u2l+en2aljg6u7pBV9/coHKpVMnaWVDmLx4HoEoSsapHn37y5V1dHZGI0o2qtNH\nDil95nF8/15auzWLslh1mY49e0Or0sYV5eU4e0TxfD1zeD8qKm1sYGiEVh1VX3Th4uEJrwbkXL1+\n/iyKCuXbOCY6Eq8iybnauZfs+u4WVla02vLPHtxDJl/xQnwA+PfcaZSVkedBQCP1Zcrg4ODg+Bhw\nAXkODg4OjnrHc0Y6O1lERpIrh/39/T/a6kV/f3/o6UnrDhYWFuLly5dKesjGzs4OjpX1GcViMe3z\n1Ge8vb3Rty/5AObMmTM4c+aMTNmTJ0/i7FmyduaXX34JCzkBz2fPnqFFixbEa9u2bQr1oGYXyM7O\nxrx582RmZkhMTMSCBQuIwKubmxsXkFeCl7cPevcjH86cP30K506fkil75sRxnD9zmmh/8dU3sJCT\nRjTs2VO0a9aYeO3cukWhHj/9PIU4zsnOxsLffkVpafWsFkmJCVg8fy5hY1c3NwwYIj8gf+n8Oaxa\nuoSQ9/bxxcat2+XqXR/x8PJG997kw5nL587i0jnZ8/jC6VO4cp4MVg//3//kLlx4HhaKnm1bEa99\nShZzjJkwkTjOzcnBioXzZc7j5KRErFr8O2EzZ1dX9FUQkJ87fSqiIsJZLbiKigjHzJ8nQFiZelFL\nSwv/G/2d0n6aiIe9Lbo3JhdBXA6NxKVnsn+vL4RE4Eoo+bs2vH1LmBvLz2Tz/H0ies5bRbz23biv\nPsVrwPJj5/Dw1RtIJIrrmAtEYqw/exUXn5IPLJ2sLdGnuWbv5FIHduYmuDRnHPGa2l/xw+R9d5+i\nrPL7NuDp4tfB3Wl13KuwMDLEnCE9YMCTpqsvLBXgxBPF14t7bpG78axMjDBrcDfo83SryTlZmmHG\nwK7EgpOUnDxcfR6j+IPWcdy9vNCV8hD9yvlzuHJedj3yS2dO4yolS8ywUfL9dGRYKPp2aEO8Duza\noVCP736i++lVvy+EQIafTklKxOoli0g/7eKK3gO4gHxt8HZ3Q9+u5Lw7c+UaTl/5V6bsyUtXcPYq\nWcf8q2FDYGkuf1HMs8goNO87mHhtO3BYoS4/f/cNcZydm4e5q9agVCCoJpeYkor5q9eR19vOThjc\nW/4i3J/nL8KzyCilv9N5BQWYuWwVHjwlF3o0Cw5Cu+bVSyFoCj6eHujXk8wEdfriZZy6IDv7xIlz\nF3D60mWi/fXI4bC0kF9W6Wl4BJp06UG8tuxRHCCeMu4H4jg7NxdzliyXeR2WkJyMectXkvZ1ccYQ\nBQH5Sb/+hjAW9gWAsMgojJs2AwLKddgPX3+ptF9dwM3TC516kAuRr188j2sXZWf1unruDK5fukC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vrxkvT1118bjsv68f/bWrZsaXgv1Ob68agf1m7YaHgcGOCv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\nyJkkWGlpqSZOnOjgKG1r2ODBxvt/xG2zOF18UnKySbJ2+JAhNomje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DEm33/7YDoSPZZXKMGRGC8aFkgE5mdg4Ki4ulbNF3uBl9BWUl5FgXv/s+9PT1xdrqGxhg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\nA8uWLcOFCxfA4/FQVFSE8PBwDBkyBDY2NqitrUViYiIqKiq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5YqNyNdR/fu1ndrPtTkuTdo5JTp7dTCtpGabvo3N7SVvtRZYSEhjeq5kIB8Vk6O6efYwnacbcvZ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\nDsVEdM1gvKQWkx2Te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B+0svJfb9yE0BH0kqwmpqb4dPWX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eQpYau/kpKSMHfuXLx8+RIA1E7GAzX9m5qaqunQiBrc3d05t7W0tMTVq1elBqkrsmfPHvz0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\nHnk177hBAyg/FixYoPfee09S0T+7zl4pBUWX33tamPea9xewjr+/v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            "text/plain": [
              "<Figure size 1152x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 1010,
              "height": 826
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O3YeRR5Dtan5",
        "colab_type": "text"
      },
      "source": [
        "# Preprocessing\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uQ9aGr_ptdeD",
        "colab_type": "text"
      },
      "source": [
        "## Missing data?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eeIt_qd6DrKk",
        "colab_type": "code",
        "outputId": "8364b35d-c624-4829-febb-2ff3b0933c6a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 107
        }
      },
      "source": [
        "missing = df.isnull().sum()\n",
        "missing[missing > 0].sort_values(ascending=False)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "reviews_per_month    10052\n",
              "last_review          10052\n",
              "host_name               21\n",
              "name                    16\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p6MQS4KMbDe0",
        "colab_type": "text"
      },
      "source": [
        "## Remove unused columns!\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kW6U82pubB3h",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = df.drop(['id', 'name', 'host_id', 'host_name', 'reviews_per_month', 'last_review', 'neighbourhood'], axis=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SnS0QVFoHWOb",
        "colab_type": "code",
        "outputId": "2c1cbd3a-6677-44b7-cb56-f4ec24da9344",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "missing = df.isnull().sum()\n",
        "missing[missing > 0].sort_values(ascending=False)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Series([], dtype: int64)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BvbyYtpxHWcM",
        "colab_type": "code",
        "outputId": "6abb547f-4fa5-4467-f006-68f57b008356",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        }
      },
      "source": [
        "df.columns"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['neighbourhood_group', 'latitude', 'longitude', 'room_type', 'price',\n",
              "       'minimum_nights', 'number_of_reviews', 'calculated_host_listings_count',\n",
              "       'availability_365'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mric5PQz_KYr",
        "colab_type": "code",
        "outputId": "2fac124c-1cb2-4529-e6f0-59f32b53fd4e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>neighbourhood_group</th>\n",
              "      <th>latitude</th>\n",
              "      <th>longitude</th>\n",
              "      <th>room_type</th>\n",
              "      <th>price</th>\n",
              "      <th>minimum_nights</th>\n",
              "      <th>number_of_reviews</th>\n",
              "      <th>calculated_host_listings_count</th>\n",
              "      <th>availability_365</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Brooklyn</td>\n",
              "      <td>40.64749</td>\n",
              "      <td>-73.97237</td>\n",
              "      <td>Private room</td>\n",
              "      <td>149</td>\n",
              "      <td>1</td>\n",
              "      <td>9</td>\n",
              "      <td>6</td>\n",
              "      <td>365</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Manhattan</td>\n",
              "      <td>40.75362</td>\n",
              "      <td>-73.98377</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>225</td>\n",
              "      <td>1</td>\n",
              "      <td>45</td>\n",
              "      <td>2</td>\n",
              "      <td>355</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Manhattan</td>\n",
              "      <td>40.80902</td>\n",
              "      <td>-73.94190</td>\n",
              "      <td>Private room</td>\n",
              "      <td>150</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>365</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Brooklyn</td>\n",
              "      <td>40.68514</td>\n",
              "      <td>-73.95976</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>89</td>\n",
              "      <td>1</td>\n",
              "      <td>270</td>\n",
              "      <td>1</td>\n",
              "      <td>194</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Manhattan</td>\n",
              "      <td>40.79851</td>\n",
              "      <td>-73.94399</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>80</td>\n",
              "      <td>10</td>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  neighbourhood_group  ...  availability_365\n",
              "0            Brooklyn  ...               365\n",
              "1           Manhattan  ...               355\n",
              "2           Manhattan  ...               365\n",
              "3            Brooklyn  ...               194\n",
              "4           Manhattan  ...                 0\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FO6Pa1xLtmdp",
        "colab_type": "text"
      },
      "source": [
        "## Predict log1p"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MYW-Od-xdsyl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X = df.drop('price', axis=1)\n",
        "y = np.log1p(df.price.values)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cXy9TGLJtocr",
        "colab_type": "text"
      },
      "source": [
        "## Feature scaling and One-hot encoding"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FSeKlMFMGGZ9",
        "colab_type": "code",
        "outputId": "6566bb62-1498-4c0f-dc4d-42d2081510aa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        }
      },
      "source": [
        "from sklearn.preprocessing import OneHotEncoder\n",
        "\n",
        "data = [['Manhattan'], ['Brooklyn']]\n",
        "\n",
        "OneHotEncoder(sparse=False).fit_transform(data)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0., 1.],\n",
              "       [1., 0.]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2G33Oli2cPc7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.preprocessing import MinMaxScaler, OneHotEncoder\n",
        "from sklearn.compose import make_column_transformer\n",
        "\n",
        "transformer = make_column_transformer(\n",
        "    (MinMaxScaler(), ['latitude', 'longitude', 'minimum_nights', 'number_of_reviews', 'calculated_host_listings_count', 'availability_365']),\n",
        "    (OneHotEncoder(handle_unknown=\"ignore\"), ['neighbourhood_group', 'room_type'])\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CRLy71G_c9H6",
        "colab_type": "code",
        "outputId": "4feafc58-ae6f-422c-d554-8a082943a44b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 305
        }
      },
      "source": [
        "transformer.fit(X)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ColumnTransformer(n_jobs=None, remainder='drop', sparse_threshold=0.3,\n",
              "                  transformer_weights=None,\n",
              "                  transformers=[('minmaxscaler',\n",
              "                                 MinMaxScaler(copy=True, feature_range=(0, 1)),\n",
              "                                 ['latitude', 'longitude', 'minimum_nights',\n",
              "                                  'number_of_reviews',\n",
              "                                  'calculated_host_listings_count',\n",
              "                                  'availability_365']),\n",
              "                                ('onehotencoder',\n",
              "                                 OneHotEncoder(categorical_features=None,\n",
              "                                               categories=None, drop=None,\n",
              "                                               dtype=<class 'numpy.float64'>,\n",
              "                                               handle_unknown='ignore',\n",
              "                                               n_values=None, sparse=True),\n",
              "                                 ['neighbourhood_group', 'room_type'])],\n",
              "                  verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f-aA3uWdg6LL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X = transformer.transform(X)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QjeMQL-CttXn",
        "colab_type": "text"
      },
      "source": [
        "## Split the training and test data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d6A-krkbIk5I",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=RANDOM_SEED)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "piEVDe_9LfA-",
        "colab_type": "code",
        "outputId": "1313ca40-f317-483f-b48c-220af011049a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "X_train.shape"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(39116, 14)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vfP1CI2VtvrG",
        "colab_type": "text"
      },
      "source": [
        "# Modeling"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gvCBLfRtlGPp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def plot_mse(history):\n",
        "  hist = pd.DataFrame(history.history)\n",
        "  hist['epoch'] = history.epoch\n",
        "\n",
        "  plt.figure()\n",
        "  plt.xlabel('Epoch')\n",
        "  plt.ylabel('MSE')\n",
        "  plt.plot(hist['epoch'], hist['mse'],\n",
        "            label='Train MSE')\n",
        "  plt.plot(hist['epoch'], hist['val_mse'],\n",
        "            label = 'Val MSE')\n",
        "  plt.legend()\n",
        "  plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E-jgzdl5ofgp",
        "colab_type": "code",
        "outputId": "130f9a0b-b71f-4f03-a81c-ef983a015ff8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "model = keras.Sequential()\n",
        "model.add(keras.layers.Dense(units=64, activation=\"relu\", input_shape=[X_train.shape[1]]))\n",
        "model.add(keras.layers.Dropout(rate=0.3))\n",
        "model.add(keras.layers.Dense(units=32, activation=\"relu\"))\n",
        "model.add(keras.layers.Dropout(rate=0.5))\n",
        "\n",
        "model.add(keras.layers.Dense(1))\n",
        "\n",
        "model.compile(\n",
        "    optimizer=keras.optimizers.Adam(0.0001),\n",
        "    loss = 'mse', \n",
        "    metrics = ['mse'])\n",
        "\n",
        "BATCH_SIZE = 32\n",
        "\n",
        "early_stop = keras.callbacks.EarlyStopping(\n",
        "  monitor='val_mse',\n",
        "  mode=\"min\",\n",
        "  patience=10\n",
        ")\n",
        "\n",
        "history = model.fit(\n",
        "  x=X_train,\n",
        "  y=y_train,\n",
        "  shuffle=True,\n",
        "  epochs=100,\n",
        "  validation_split=0.2,\n",
        "  batch_size=BATCH_SIZE,\n",
        "  callbacks=[early_stop]\n",
        ")\n",
        "\n",
        "plot_mse(history)"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 31292 samples, validate on 7824 samples\n",
            "Epoch 1/100\n",
            "31292/31292 [==============================] - 6s 181us/sample - loss: 8.7079 - mse: 8.7079 - val_loss: 0.5533 - val_mse: 0.5533\n",
            "Epoch 2/100\n",
            "31292/31292 [==============================] - 4s 135us/sample - loss: 2.3689 - mse: 2.3689 - val_loss: 0.4569 - val_mse: 0.4569\n",
            "Epoch 3/100\n",
            "31292/31292 [==============================] - 4s 134us/sample - loss: 2.1296 - mse: 2.1296 - val_loss: 0.4418 - val_mse: 0.4418\n",
            "Epoch 4/100\n",
            "31292/31292 [==============================] - 4s 134us/sample - loss: 2.0007 - mse: 2.0007 - val_loss: 0.4275 - val_mse: 0.4275\n",
            "Epoch 5/100\n",
            "31292/31292 [==============================] - 4s 135us/sample - loss: 1.9427 - mse: 1.9427 - val_loss: 0.4257 - val_mse: 0.4257\n",
            "Epoch 6/100\n",
            "31292/31292 [==============================] - 4s 135us/sample - loss: 1.8758 - mse: 1.8758 - val_loss: 0.4258 - val_mse: 0.4258\n",
            "Epoch 7/100\n",
            "31292/31292 [==============================] - 4s 135us/sample - loss: 1.8158 - mse: 1.8158 - val_loss: 0.3888 - val_mse: 0.3888\n",
            "Epoch 8/100\n",
            "31292/31292 [==============================] - 4s 133us/sample - loss: 1.6837 - mse: 1.6837 - val_loss: 0.3625 - val_mse: 0.3625\n",
            "Epoch 9/100\n",
            "31292/31292 [==============================] - 4s 138us/sample - loss: 1.6104 - mse: 1.6104 - val_loss: 0.3858 - val_mse: 0.3858\n",
            "Epoch 10/100\n",
            "31292/31292 [==============================] - 4s 135us/sample - loss: 1.5451 - mse: 1.5451 - val_loss: 0.3899 - val_mse: 0.3899\n",
            "Epoch 11/100\n",
            "31292/31292 [==============================] - 4s 139us/sample - loss: 1.4726 - mse: 1.4726 - val_loss: 0.3143 - val_mse: 0.3143\n",
            "Epoch 12/100\n",
            "31292/31292 [==============================] - 4s 136us/sample - loss: 1.4200 - mse: 1.4200 - val_loss: 0.3296 - val_mse: 0.3296\n",
            "Epoch 13/100\n",
            "31292/31292 [==============================] - 4s 134us/sample - loss: 1.3764 - mse: 1.3764 - val_loss: 0.3677 - val_mse: 0.3677\n",
            "Epoch 14/100\n",
            "31292/31292 [==============================] - 4s 136us/sample - loss: 1.3316 - mse: 1.3316 - val_loss: 0.3473 - val_mse: 0.3473\n",
            "Epoch 15/100\n",
            "31292/31292 [==============================] - 4s 134us/sample - loss: 1.2729 - mse: 1.2729 - val_loss: 0.3307 - val_mse: 0.3307\n",
            "Epoch 16/100\n",
            "31292/31292 [==============================] - 4s 135us/sample - loss: 1.2420 - mse: 1.2420 - val_loss: 0.3226 - val_mse: 0.3226\n",
            "Epoch 17/100\n",
            "31292/31292 [==============================] - 4s 134us/sample - loss: 1.1660 - mse: 1.1660 - val_loss: 0.3241 - val_mse: 0.3241\n",
            "Epoch 18/100\n",
            "31292/31292 [==============================] - 4s 140us/sample - loss: 1.0746 - mse: 1.0746 - val_loss: 0.3197 - val_mse: 0.3197\n",
            "Epoch 19/100\n",
            "31292/31292 [==============================] - 5s 146us/sample - loss: 1.0228 - mse: 1.0228 - val_loss: 0.2883 - val_mse: 0.2883\n",
            "Epoch 20/100\n",
            "31292/31292 [==============================] - 4s 134us/sample - loss: 0.9810 - mse: 0.9810 - val_loss: 0.3102 - val_mse: 0.3102\n",
            "Epoch 21/100\n",
            "31292/31292 [==============================] - 4s 137us/sample - loss: 0.9548 - mse: 0.9548 - val_loss: 0.3103 - val_mse: 0.3103\n",
            "Epoch 22/100\n",
            "31292/31292 [==============================] - 4s 136us/sample - loss: 0.9163 - mse: 0.9163 - val_loss: 0.3103 - val_mse: 0.3103\n",
            "Epoch 23/100\n",
            "31292/31292 [==============================] - 4s 137us/sample - loss: 0.8813 - mse: 0.8813 - val_loss: 0.2961 - val_mse: 0.2961\n",
            "Epoch 24/100\n",
            "31292/31292 [==============================] - 4s 136us/sample - loss: 0.8493 - mse: 0.8493 - val_loss: 0.3075 - val_mse: 0.3075\n",
            "Epoch 25/100\n",
            "31292/31292 [==============================] - 4s 136us/sample - loss: 0.8289 - mse: 0.8289 - val_loss: 0.2805 - val_mse: 0.2805\n",
            "Epoch 26/100\n",
            "31292/31292 [==============================] - 4s 143us/sample - loss: 0.7931 - mse: 0.7931 - val_loss: 0.2980 - val_mse: 0.2980\n",
            "Epoch 27/100\n",
            "31292/31292 [==============================] - 5s 148us/sample - loss: 0.7599 - mse: 0.7599 - val_loss: 0.2825 - val_mse: 0.2825\n",
            "Epoch 28/100\n",
            "31292/31292 [==============================] - 4s 140us/sample - loss: 0.7442 - mse: 0.7442 - val_loss: 0.2762 - val_mse: 0.2762\n",
            "Epoch 29/100\n",
            "31292/31292 [==============================] - 4s 134us/sample - loss: 0.7204 - mse: 0.7204 - val_loss: 0.2984 - val_mse: 0.2984\n",
            "Epoch 30/100\n",
            "31292/31292 [==============================] - 4s 137us/sample - loss: 0.6912 - mse: 0.6912 - val_loss: 0.2750 - val_mse: 0.2750\n",
            "Epoch 31/100\n",
            "31292/31292 [==============================] - 4s 137us/sample - loss: 0.6574 - mse: 0.6574 - val_loss: 0.2773 - val_mse: 0.2773\n",
            "Epoch 32/100\n",
            "31292/31292 [==============================] - 4s 135us/sample - loss: 0.6376 - mse: 0.6376 - val_loss: 0.2754 - val_mse: 0.2754\n",
            "Epoch 33/100\n",
            "31292/31292 [==============================] - 4s 135us/sample - loss: 0.6188 - mse: 0.6188 - val_loss: 0.2689 - val_mse: 0.2689\n",
            "Epoch 34/100\n",
            "31292/31292 [==============================] - 4s 130us/sample - loss: 0.5944 - mse: 0.5944 - val_loss: 0.2733 - val_mse: 0.2733\n",
            "Epoch 35/100\n",
            "31292/31292 [==============================] - 4s 137us/sample - loss: 0.5769 - mse: 0.5769 - val_loss: 0.2699 - val_mse: 0.2699\n",
            "Epoch 36/100\n",
            "31292/31292 [==============================] - 4s 134us/sample - loss: 0.5567 - mse: 0.5567 - val_loss: 0.2687 - val_mse: 0.2687\n",
            "Epoch 37/100\n",
            "31292/31292 [==============================] - 4s 137us/sample - loss: 0.5446 - mse: 0.5446 - val_loss: 0.2737 - val_mse: 0.2737\n",
            "Epoch 38/100\n",
            "31292/31292 [==============================] - 4s 136us/sample - loss: 0.5215 - mse: 0.5215 - val_loss: 0.2693 - val_mse: 0.2693\n",
            "Epoch 39/100\n",
            "31292/31292 [==============================] - 4s 133us/sample - loss: 0.5096 - mse: 0.5096 - val_loss: 0.2647 - val_mse: 0.2647\n",
            "Epoch 40/100\n",
            "31292/31292 [==============================] - 4s 136us/sample - loss: 0.4977 - mse: 0.4977 - val_loss: 0.2753 - val_mse: 0.2753\n",
            "Epoch 41/100\n",
            "31292/31292 [==============================] - 4s 137us/sample - loss: 0.4800 - mse: 0.4800 - val_loss: 0.2656 - val_mse: 0.2656\n",
            "Epoch 42/100\n",
            "31292/31292 [==============================] - 4s 138us/sample - loss: 0.4739 - mse: 0.4739 - val_loss: 0.2611 - val_mse: 0.2611\n",
            "Epoch 43/100\n",
            "31292/31292 [==============================] - 4s 140us/sample - loss: 0.4549 - mse: 0.4549 - val_loss: 0.2631 - val_mse: 0.2631\n",
            "Epoch 44/100\n",
            "31292/31292 [==============================] - 4s 136us/sample - loss: 0.4442 - mse: 0.4442 - val_loss: 0.2625 - val_mse: 0.2625\n",
            "Epoch 45/100\n",
            "31292/31292 [==============================] - 4s 134us/sample - loss: 0.4253 - mse: 0.4253 - val_loss: 0.2568 - val_mse: 0.2568\n",
            "Epoch 46/100\n",
            "31292/31292 [==============================] - 4s 134us/sample - loss: 0.4152 - mse: 0.4152 - val_loss: 0.2591 - val_mse: 0.2591\n",
            "Epoch 47/100\n",
            "31292/31292 [==============================] - 4s 132us/sample - loss: 0.4107 - mse: 0.4107 - val_loss: 0.2609 - val_mse: 0.2609\n",
            "Epoch 48/100\n",
            "31292/31292 [==============================] - 6s 177us/sample - loss: 0.3931 - mse: 0.3931 - val_loss: 0.2573 - val_mse: 0.2573\n",
            "Epoch 49/100\n",
            "31292/31292 [==============================] - 5s 146us/sample - loss: 0.3858 - mse: 0.3858 - val_loss: 0.2579 - val_mse: 0.2579\n",
            "Epoch 50/100\n",
            "31292/31292 [==============================] - 4s 140us/sample - loss: 0.3730 - mse: 0.3730 - val_loss: 0.2531 - val_mse: 0.2531\n",
            "Epoch 51/100\n",
            "31292/31292 [==============================] - 5s 146us/sample - loss: 0.3657 - mse: 0.3657 - val_loss: 0.2527 - val_mse: 0.2527\n",
            "Epoch 52/100\n",
            "31292/31292 [==============================] - 4s 134us/sample - loss: 0.3617 - mse: 0.3617 - val_loss: 0.2560 - val_mse: 0.2560\n",
            "Epoch 53/100\n",
            "31292/31292 [==============================] - 4s 133us/sample - loss: 0.3513 - mse: 0.3513 - val_loss: 0.2562 - val_mse: 0.2562\n",
            "Epoch 54/100\n",
            "31292/31292 [==============================] - 4s 137us/sample - loss: 0.3468 - mse: 0.3468 - val_loss: 0.2524 - val_mse: 0.2524\n",
            "Epoch 55/100\n",
            "31292/31292 [==============================] - 5s 152us/sample - loss: 0.3385 - mse: 0.3385 - val_loss: 0.2512 - val_mse: 0.2512\n",
            "Epoch 56/100\n",
            "31292/31292 [==============================] - 5s 145us/sample - loss: 0.3326 - mse: 0.3326 - val_loss: 0.2532 - val_mse: 0.2532\n",
            "Epoch 57/100\n",
            "31292/31292 [==============================] - 4s 135us/sample - loss: 0.3258 - mse: 0.3258 - val_loss: 0.2507 - val_mse: 0.2507\n",
            "Epoch 58/100\n",
            "31292/31292 [==============================] - 4s 137us/sample - loss: 0.3220 - mse: 0.3220 - val_loss: 0.2527 - val_mse: 0.2527\n",
            "Epoch 59/100\n",
            "31292/31292 [==============================] - 4s 136us/sample - loss: 0.3130 - mse: 0.3130 - val_loss: 0.2523 - val_mse: 0.2523\n",
            "Epoch 60/100\n",
            "31292/31292 [==============================] - 4s 133us/sample - loss: 0.3126 - mse: 0.3126 - val_loss: 0.2509 - val_mse: 0.2509\n",
            "Epoch 61/100\n",
            "31292/31292 [==============================] - 4s 134us/sample - loss: 0.3031 - mse: 0.3031 - val_loss: 0.2507 - val_mse: 0.2507\n",
            "Epoch 62/100\n",
            "31292/31292 [==============================] - 4s 133us/sample - loss: 0.2980 - mse: 0.2980 - val_loss: 0.2477 - val_mse: 0.2477\n",
            "Epoch 63/100\n",
            "31292/31292 [==============================] - 4s 135us/sample - loss: 0.2968 - mse: 0.2968 - val_loss: 0.2492 - val_mse: 0.2492\n",
            "Epoch 64/100\n",
            "31292/31292 [==============================] - 4s 135us/sample - loss: 0.2952 - mse: 0.2952 - val_loss: 0.2475 - val_mse: 0.2475\n",
            "Epoch 65/100\n",
            "31292/31292 [==============================] - 4s 134us/sample - loss: 0.2879 - mse: 0.2879 - val_loss: 0.2464 - val_mse: 0.2464\n",
            "Epoch 66/100\n",
            "31292/31292 [==============================] - 4s 134us/sample - loss: 0.2838 - mse: 0.2838 - val_loss: 0.2482 - val_mse: 0.2482\n",
            "Epoch 67/100\n",
            "31292/31292 [==============================] - 5s 149us/sample - loss: 0.2826 - mse: 0.2826 - val_loss: 0.2475 - val_mse: 0.2475\n",
            "Epoch 68/100\n",
            "31292/31292 [==============================] - 4s 137us/sample - loss: 0.2809 - mse: 0.2809 - val_loss: 0.2449 - val_mse: 0.2449\n",
            "Epoch 69/100\n",
            "31292/31292 [==============================] - 4s 135us/sample - loss: 0.2776 - mse: 0.2776 - val_loss: 0.2473 - val_mse: 0.2473\n",
            "Epoch 70/100\n",
            "31292/31292 [==============================] - 4s 136us/sample - loss: 0.2743 - mse: 0.2743 - val_loss: 0.2424 - val_mse: 0.2424\n",
            "Epoch 71/100\n",
            "31292/31292 [==============================] - 4s 138us/sample - loss: 0.2749 - mse: 0.2749 - val_loss: 0.2427 - val_mse: 0.2427\n",
            "Epoch 72/100\n",
            "31292/31292 [==============================] - 4s 133us/sample - loss: 0.2711 - mse: 0.2711 - val_loss: 0.2472 - val_mse: 0.2472\n",
            "Epoch 73/100\n",
            "31292/31292 [==============================] - 4s 133us/sample - loss: 0.2663 - mse: 0.2663 - val_loss: 0.2438 - val_mse: 0.2438\n",
            "Epoch 74/100\n",
            "31292/31292 [==============================] - 4s 132us/sample - loss: 0.2660 - mse: 0.2660 - val_loss: 0.2427 - val_mse: 0.2427\n",
            "Epoch 75/100\n",
            "31292/31292 [==============================] - 4s 133us/sample - loss: 0.2653 - mse: 0.2653 - val_loss: 0.2428 - val_mse: 0.2428\n",
            "Epoch 76/100\n",
            "31292/31292 [==============================] - 4s 131us/sample - loss: 0.2644 - mse: 0.2644 - val_loss: 0.2428 - val_mse: 0.2428\n",
            "Epoch 77/100\n",
            "31292/31292 [==============================] - 4s 133us/sample - loss: 0.2632 - mse: 0.2632 - val_loss: 0.2431 - val_mse: 0.2431\n",
            "Epoch 78/100\n",
            "31292/31292 [==============================] - 4s 132us/sample - loss: 0.2613 - mse: 0.2613 - val_loss: 0.2416 - val_mse: 0.2416\n",
            "Epoch 79/100\n",
            "31292/31292 [==============================] - 4s 134us/sample - loss: 0.2584 - mse: 0.2584 - val_loss: 0.2406 - val_mse: 0.2406\n",
            "Epoch 80/100\n",
            "31292/31292 [==============================] - 4s 133us/sample - loss: 0.2581 - mse: 0.2581 - val_loss: 0.2408 - val_mse: 0.2408\n",
            "Epoch 81/100\n",
            "31292/31292 [==============================] - 4s 136us/sample - loss: 0.2562 - mse: 0.2562 - val_loss: 0.2398 - val_mse: 0.2398\n",
            "Epoch 82/100\n",
            "31292/31292 [==============================] - 4s 134us/sample - loss: 0.2571 - mse: 0.2571 - val_loss: 0.2408 - val_mse: 0.2408\n",
            "Epoch 83/100\n",
            "31292/31292 [==============================] - 4s 133us/sample - loss: 0.2525 - mse: 0.2525 - val_loss: 0.2389 - val_mse: 0.2389\n",
            "Epoch 84/100\n",
            "31292/31292 [==============================] - 4s 133us/sample - loss: 0.2538 - mse: 0.2538 - val_loss: 0.2398 - val_mse: 0.2398\n",
            "Epoch 85/100\n",
            "31292/31292 [==============================] - 4s 133us/sample - loss: 0.2527 - mse: 0.2527 - val_loss: 0.2400 - val_mse: 0.2400\n",
            "Epoch 86/100\n",
            "31292/31292 [==============================] - 4s 132us/sample - loss: 0.2540 - mse: 0.2540 - val_loss: 0.2393 - val_mse: 0.2393\n",
            "Epoch 87/100\n",
            "31292/31292 [==============================] - 4s 133us/sample - loss: 0.2533 - mse: 0.2533 - val_loss: 0.2379 - val_mse: 0.2379\n",
            "Epoch 88/100\n",
            "31292/31292 [==============================] - 4s 132us/sample - loss: 0.2512 - mse: 0.2512 - val_loss: 0.2391 - val_mse: 0.2391\n",
            "Epoch 89/100\n",
            "31292/31292 [==============================] - 4s 134us/sample - loss: 0.2497 - mse: 0.2497 - val_loss: 0.2387 - val_mse: 0.2387\n",
            "Epoch 90/100\n",
            "31292/31292 [==============================] - 5s 146us/sample - loss: 0.2517 - mse: 0.2517 - val_loss: 0.2384 - val_mse: 0.2384\n",
            "Epoch 91/100\n",
            "31292/31292 [==============================] - 4s 139us/sample - loss: 0.2512 - mse: 0.2512 - val_loss: 0.2389 - val_mse: 0.2389\n",
            "Epoch 92/100\n",
            "31292/31292 [==============================] - 4s 137us/sample - loss: 0.2494 - mse: 0.2494 - val_loss: 0.2386 - val_mse: 0.2386\n",
            "Epoch 93/100\n",
            "31292/31292 [==============================] - 4s 135us/sample - loss: 0.2487 - mse: 0.2487 - val_loss: 0.2368 - val_mse: 0.2368\n",
            "Epoch 94/100\n",
            "31292/31292 [==============================] - 5s 160us/sample - loss: 0.2490 - mse: 0.2490 - val_loss: 0.2379 - val_mse: 0.2379\n",
            "Epoch 95/100\n",
            "31292/31292 [==============================] - 4s 137us/sample - loss: 0.2486 - mse: 0.2486 - val_loss: 0.2391 - val_mse: 0.2391\n",
            "Epoch 96/100\n",
            "31292/31292 [==============================] - 4s 135us/sample - loss: 0.2490 - mse: 0.2490 - val_loss: 0.2395 - val_mse: 0.2395\n",
            "Epoch 97/100\n",
            "31292/31292 [==============================] - 4s 135us/sample - loss: 0.2461 - mse: 0.2461 - val_loss: 0.2367 - val_mse: 0.2367\n",
            "Epoch 98/100\n",
            "31292/31292 [==============================] - 4s 138us/sample - loss: 0.2476 - mse: 0.2476 - val_loss: 0.2382 - val_mse: 0.2382\n",
            "Epoch 99/100\n",
            "31292/31292 [==============================] - 5s 147us/sample - loss: 0.2467 - mse: 0.2467 - val_loss: 0.2389 - val_mse: 0.2389\n",
            "Epoch 100/100\n",
            "31292/31292 [==============================] - 4s 143us/sample - loss: 0.2470 - mse: 0.2470 - val_loss: 0.2375 - val_mse: 0.2375\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
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64HujrX6Ahtk/XphabCcOOPZa20VAu0VAs0uc5xxkQ9c9vz4hbS9YmhggZ6XBlDT1wA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\nANhCBLQAcJvc4YPq/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            "text/plain": [
              "<Figure size 1152x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 948,
              "height": 605
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ffiebIFwa9zD",
        "colab_type": "text"
      },
      "source": [
        "# Evaluation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WGy2jD2qJ5u_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.metrics import mean_squared_error\n",
        "from sklearn.metrics import mean_absolute_error\n",
        "from math import sqrt\n",
        "from sklearn.metrics import r2_score"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Imxz47Gub0Hl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_pred = model.predict(X_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yl4C_QuKBaGm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "4a537b5e-b6dd-4496-8f3a-a13f1eecd92f"
      },
      "source": [
        "f'MSE {mean_squared_error(y_test, y_pred)}'"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'MSE 0.21402013564287092'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tliN2dqyb2FX",
        "colab_type": "code",
        "outputId": "590497ab-bbe4-44ef-9b67-066df8e42545",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "f'MAE {mean_absolute_error(y_test, y_pred)}'"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'MAE 0.33315857508453867'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VskCCdLFc-6k",
        "colab_type": "code",
        "outputId": "55e944e6-742e-4f25-b342-05d077491639",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "f'RMSE {np.sqrt(mean_squared_error(y_test, y_pred))}'"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'RMSE 0.4626231032307735'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g_KcC-mNdBF9",
        "colab_type": "code",
        "outputId": "8a975c49-0cfc-4420-b7a3-abb35d6ebc24",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "f'R2 {r2_score(y_test, y_pred)}'"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'R2 0.5476099980350737'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U-YB3UBsiELo",
        "colab_type": "text"
      },
      "source": [
        "# Saving"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gDyy6MMQszxX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "joblib.dump(transformer, \"data_transformer.joblib\")\n",
        "model.save(\"price_prediction_model.h5\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0xv7dFQ3LPXv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from google.colab import files"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VXzinOHpfy77",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "files.download(\"data_transformer.joblib\")\n",
        "files.download(\"price_prediction_model.h5\") "
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}